Mathematical Expression Recognition

Mathematical expression recognition (MER) aims to automatically convert images of handwritten or printed mathematical formulas into machine-readable formats like LaTeX. Current research heavily focuses on improving accuracy and robustness, particularly for complex formulas and real-world scenarios, using deep learning models such as convolutional neural networks (CNNs), transformers, and hybrid architectures incorporating attention mechanisms and hierarchical structures. These advancements are driven by the creation of larger, more diverse datasets and the development of novel techniques to handle variations in handwriting styles, fonts, and formula complexity. Improved MER has significant implications for automating scientific document processing, improving accessibility for individuals with disabilities, and facilitating the development of intelligent tutoring systems.